TITLE:
Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach
AUTHORS:
Artemio Sotomayor-Olmedo, Marco A. Aceves-Fernández, Efrén Gorrostieta-Hurtado, Carlos Pedraza-Ortega, Juan M. Ramos-Arreguín, J. Emilio Vargas-Soto
KEYWORDS:
Predictive Models; Airborne Pollution; Support Vector Machines; Kernel Functions
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.3 No.3,
July
5,
2013
ABSTRACT: The development of forecasting models for pollution particles shows
a nonlinear dynamic behavior; hence, implementation is a non-trivial process.
In the literature, there have been multiple models of particulate pollutants,
which use softcomputing techniques and machine learning such as: multilayer
perceptrons, neural networks, support vector machines, kernel algorithms, and
so on. This paper presents a prediction pollution model using support vector
machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally,
the prediction results of ozone (O3), particulate matter (PM10) and
nitrogen dioxide (NO2) at Mexico City are presented as a case study
using these techniques.